{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XXDeo-aGOAXF"
   },
   "source": [
    "##### Copyright 2020 The TensorFlow Authors.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9XRGdjHNOE9D"
   },
   "outputs": [],
   "source": [
    "#@title ##### Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KJihamFwOLUT"
   },
   "source": [
    "# Bayesian Neural Network\n",
    "\n",
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/python/experimental/nn/examples/bnn_mnist_advi.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/experimental/nn/examples/bnn_mnist_advi.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B0HrNKbJw2bA"
   },
   "source": [
    "### 1  Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "cttwhYKYGhPj"
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics as sklearn_metrics\n",
    "\n",
    "import tensorflow.compat.v2 as tf\n",
    "tf.enable_v2_behavior()\n",
    "\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_probability as tfp\n",
    "\n",
    "from tensorflow_probability.python.internal import prefer_static\n",
    "\n",
    "# Globally Enable XLA.\n",
    "# tf.config.optimizer.set_jit(True)\n",
    "\n",
    "try:\n",
    "  physical_devices = tf.config.list_physical_devices('GPU')\n",
    "  tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
    "except:\n",
    "  # Invalid device or cannot modify virtual devices once initialized.\n",
    "  pass\n",
    "\n",
    "tfb = tfp.bijectors\n",
    "tfd = tfp.distributions\n",
    "tfn = tfp.experimental.nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nbQ3rcTowypZ"
   },
   "source": [
    "### 2  Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "both",
    "colab": {
     "height": 0
    },
    "colab_type": "code",
    "id": "rjgnFMxvG9Ab",
    "outputId": "9d7d327f-aa0d-4a43-e3a9-5426cf26a37e"
   },
   "outputs": [
    {
     "data": {
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hWceOHQGkXhuFHBdU0SxZCpQRFYX3ESC+TnRMbNasGQDg4IMP3mr92cJiARMnTgxtvBcX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\n+AHZGGOMMcYY4f8BsW7a512I12MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_name = 'emnist'\n",
    "batch_size = 32\n",
    "\n",
    "[train_dataset, eval_dataset], datasets_info = tfds.load(\n",
    "    name=dataset_name,\n",
    "    split=['train', 'test'],\n",
    "    with_info=True,\n",
    "    as_supervised=True,\n",
    "    shuffle_files=True)\n",
    "\n",
    "def _preprocess(image, label):\n",
    "  image = tf.cast(image, dtype=tf.float32) / 255.\n",
    "  if dataset_name == 'emnist':\n",
    "    image = tf.transpose(image, perm=[1, 0, 2])\n",
    "  label = tf.cast(label, dtype=tf.int32)\n",
    "  return image, label\n",
    "\n",
    "train_size = datasets_info.splits['train'].num_examples\n",
    "eval_size = datasets_info.splits['test'].num_examples\n",
    "num_classes = datasets_info.features['label'].num_classes\n",
    "image_shape = datasets_info.features['image'].shape\n",
    "\n",
    "if dataset_name == 'emnist':\n",
    "  import string\n",
    "  yhuman = np.array(list(string.digits +\n",
    "                         string.ascii_uppercase +\n",
    "                         string.ascii_lowercase))\n",
    "else:\n",
    "  yhuman = np.range(num_classes).astype(np.int32)\n",
    "\n",
    "if True:\n",
    "  orig_train_size = train_size\n",
    "  train_size = int(10e3)\n",
    "  train_dataset = train_dataset.shuffle(orig_train_size // 7).repeat(1).take(train_size)\n",
    "\n",
    "train_dataset = tfn.util.tune_dataset(\n",
    "    train_dataset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle_size=int(train_size  / 7),\n",
    "    preprocess_fn=_preprocess)\n",
    "\n",
    "if True:\n",
    "  orig_eval_size = eval_size\n",
    "  eval_size = int(10e3)\n",
    "  eval_dataset = eval_dataset.shuffle(orig_eval_size // 7).repeat(1).take(eval_size)\n",
    "\n",
    "eval_dataset = tfn.util.tune_dataset(\n",
    "    eval_dataset,\n",
    "    repeat_count=None,\n",
    "    preprocess_fn=_preprocess)\n",
    "\n",
    "x, y = next(iter(eval_dataset.batch(10)))\n",
    "tfn.util.display_imgs(x, yhuman[y.numpy()]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sbaPm7ABwvde"
   },
   "source": [
    "### 3  Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "Gh2BHHnEGY1x"
   },
   "outputs": [],
   "source": [
    "#@title Optional Custom Posterior\n",
    "def make_posterior(\n",
    "    kernel_shape,\n",
    "    bias_shape,\n",
    "    dtype=tf.float32,\n",
    "    kernel_initializer=None,\n",
    "    bias_initializer=None,\n",
    "    kernel_name='posterior_kernel',\n",
    "    bias_name='posterior_bias'):\n",
    "  if kernel_initializer is None:\n",
    "    kernel_initializer = tf.initializers.glorot_uniform()\n",
    "  if bias_initializer is None:\n",
    "    bias_initializer = tf.zeros\n",
    "  make_loc = lambda shape, init, name: tf.Variable(  # pylint: disable=g-long-lambda\n",
    "      init(shape, dtype=dtype),\n",
    "      name=name + '_loc')\n",
    "  make_scale = lambda shape, name: tfp.util.TransformedVariable(  # pylint: disable=g-long-lambda\n",
    "      tf.fill(shape, tf.constant(0.01, dtype)),\n",
    "      tfb.Chain([tfb.Shift(1e-5), tfb.Softplus()]),\n",
    "      name=name + '_scale')\n",
    "  return tfd.JointDistributionSequential([\n",
    "      tfd.Independent(\n",
    "          tfd.Normal(loc=make_loc(kernel_shape, kernel_initializer, kernel_name),\n",
    "                     scale=make_scale(kernel_shape, kernel_name)),\n",
    "          reinterpreted_batch_ndims=prefer_static.size(kernel_shape),\n",
    "          name=kernel_name),\n",
    "      tfd.Independent(\n",
    "          tfd.Normal(loc=make_loc(bias_shape, bias_initializer, bias_name),\n",
    "                     scale=make_scale(bias_shape, bias_name)),\n",
    "          reinterpreted_batch_ndims=prefer_static.size(bias_shape),\n",
    "          name=bias_name),\n",
    "  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "lEh7kUBeGdMN"
   },
   "outputs": [],
   "source": [
    "#@title Optional Custom Prior\n",
    "def make_prior(\n",
    "    kernel_shape,\n",
    "    bias_shape,\n",
    "    dtype=tf.float32,\n",
    "    kernel_initializer=None,  # pylint: disable=unused-argument\n",
    "    bias_initializer=None,  # pylint: disable=unused-argument\n",
    "    kernel_name='prior_kernel',\n",
    "    bias_name='prior_bias'):\n",
    "  k = tfd.MixtureSameFamily(\n",
    "      tfd.Categorical(tf.zeros(3, dtype)),\n",
    "      tfd.StudentT(\n",
    "          df=[1,1.,1.], loc=[0,3,-3], scale=tf.constant([1, 10, 10], dtype)))\n",
    "          #df=[0.5, 1., 1.], loc=[0, 2, -2], scale=tf.constant([0.25, 5, 5], dtype)))\n",
    "  b = tfd.Normal(0, tf.constant(1000, dtype))\n",
    "  return tfd.JointDistributionSequential([\n",
    "      tfd.Sample(k, kernel_shape, name=kernel_name),\n",
    "      tfd.Sample(b, bias_shape, name=bias_name),\n",
    "  ]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "both",
    "colab": {
     "height": 338
    },
    "colab_type": "code",
    "id": "nhnbpf7IYBD6",
    "outputId": "7265b1e0-857f-4093-ba0f-32bef1db93bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== BNN ==================================================\n",
      "  SIZE SHAPE                TRAIN NAME                                    \n",
      "   200 [5, 5, 1, 8]         True  posterior_kernel_loc:0                  \n",
      "   200 [5, 5, 1, 8]         True  posterior_kernel_scale:0                \n",
      "     8 [8]                  True  posterior_bias_loc:0                    \n",
      "     8 [8]                  True  posterior_bias_scale:0                  \n",
      "  3200 [5, 5, 8, 16]        True  posterior_kernel_loc:0                  \n",
      "  3200 [5, 5, 8, 16]        True  posterior_kernel_scale:0                \n",
      "    16 [16]                 True  posterior_bias_loc:0                    \n",
      "    16 [16]                 True  posterior_bias_scale:0                  \n",
      " 12800 [5, 5, 16, 32]       True  posterior_kernel_loc:0                  \n",
      " 12800 [5, 5, 16, 32]       True  posterior_kernel_scale:0                \n",
      "    32 [32]                 True  posterior_bias_loc:0                    \n",
      "    32 [32]                 True  posterior_bias_scale:0                  \n",
      " 95648 [1568, 61]           True  posterior_kernel_loc:0                  \n",
      " 95648 [1568, 61]           True  posterior_kernel_scale:0                \n",
      "    61 [61]                 True  posterior_bias_loc:0                    \n",
      "    61 [61]                 True  posterior_bias_scale:0                  \n",
      "trainable size: 223930  /  0.854 MiB  /  {float32: 223930}\n"
     ]
    }
   ],
   "source": [
    "max_pool = tf.keras.layers.MaxPooling2D(  # Has no tf.Variables.\n",
    "    pool_size=(2, 2),\n",
    "    strides=(2, 2),\n",
    "    padding='SAME',\n",
    "    data_format='channels_last')\n",
    "\n",
    "def batchnorm(axis):\n",
    "  def fn(x):\n",
    "    m = tf.math.reduce_mean(x, axis=axis, keepdims=True)\n",
    "    v = tf.math.reduce_variance(x, axis=axis, keepdims=True)\n",
    "    return (x - m) / tf.math.sqrt(v)\n",
    "  return fn\n",
    "\n",
    "maybe_batchnorm = batchnorm(axis=[-4, -3, -2])\n",
    "# maybe_batchnorm = lambda x: x\n",
    "\n",
    "bnn = tfn.Sequential([\n",
    "  lambda x: 2. * tf.cast(x, tf.float32) - 1.,  # Center.\n",
    "  tfn.ConvolutionVariationalReparameterization(\n",
    "      input_size=1,\n",
    "      output_size=8,\n",
    "      filter_shape=5,\n",
    "      padding='SAME',\n",
    "      init_kernel_fn=tf.initializers.he_uniform(),\n",
    "      penalty_weight=1 / train_size,\n",
    "      # penalty_weight=1e2 / train_size,  # Layer specific \"beta\".\n",
    "      # make_posterior_fn=make_posterior,\n",
    "      # make_prior_fn=make_prior,\n",
    "      name='conv1'),\n",
    "  maybe_batchnorm,\n",
    "  tf.nn.leaky_relu,\n",
    "  tfn.ConvolutionVariationalReparameterization(\n",
    "      input_size=8,\n",
    "      output_size=16,\n",
    "      filter_shape=5,\n",
    "      padding='SAME',\n",
    "      init_kernel_fn=tf.initializers.he_uniform(),\n",
    "      penalty_weight=1 / train_size,\n",
    "      # penalty_weight=1e2 / train_size,  # Layer specific \"beta\".\n",
    "      # make_posterior_fn=make_posterior,\n",
    "      # make_prior_fn=make_prior,\n",
    "      name='conv2'),\n",
    "  maybe_batchnorm,\n",
    "  tf.nn.leaky_relu,\n",
    "  max_pool,  # [28, 28, 8] -> [14, 14, 8]\n",
    "  tfn.ConvolutionVariationalReparameterization(\n",
    "      input_size=16,\n",
    "      output_size=32,\n",
    "      filter_shape=5,\n",
    "      padding='SAME',\n",
    "      init_kernel_fn=tf.initializers.he_uniform(),\n",
    "      penalty_weight=1 / train_size,\n",
    "      # penalty_weight=1e2 / train_size,  # Layer specific \"beta\".\n",
    "      # make_posterior_fn=make_posterior,\n",
    "      # make_prior_fn=make_prior,\n",
    "      name='conv3'),\n",
    "  maybe_batchnorm,\n",
    "  tf.nn.leaky_relu,\n",
    "  max_pool,  # [14, 14, 16] -> [7, 7, 16]\n",
    "  tfn.util.flatten_rightmost(ndims=3),\n",
    "  tfn.AffineVariationalReparameterizationLocal(\n",
    "      input_size=7 * 7 * 32,\n",
    "      output_size=num_classes - 1,\n",
    "      penalty_weight=1. / train_size,\n",
    "      # make_posterior_fn=make_posterior,\n",
    "      # make_prior_fn=make_prior,\n",
    "      name='affine1'),\n",
    "  tfb.Pad(),\n",
    "  lambda x: tfd.Categorical(logits=x, dtype=tf.int32),   \n",
    "], name='BNN')\n",
    "\n",
    "# bnn_eval = tfn.Sequential([l for l in bnn.layers if l is not maybe_batchnorm],\n",
    "#                           name='bnn_eval')\n",
    "bnn_eval = bnn\n",
    "\n",
    "print(bnn.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J9XuHd6Iw7_a"
   },
   "source": [
    "### 4  Loss / Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "45AcvITA9qci"
   },
   "outputs": [],
   "source": [
    "def compute_loss_bnn(x, y, beta=1., is_eval=False):\n",
    "  d = bnn_eval(x) if is_eval else bnn(x)\n",
    "  nll = -tf.reduce_mean(d.log_prob(y), axis=-1)\n",
    "  kl = bnn.extra_loss\n",
    "  loss = nll + beta * kl\n",
    "  return loss, (nll, kl), d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zpoG0x6-AslV"
   },
   "outputs": [],
   "source": [
    "train_iter_bnn = iter(train_dataset)\n",
    "\n",
    "def train_loss_bnn():\n",
    "  x, y = next(train_iter_bnn)\n",
    "  loss, (nll, kl), _ = compute_loss_bnn(x, y)\n",
    "  return loss, (nll, kl)\n",
    "\n",
    "opt_bnn = tf.optimizers.Adam(learning_rate=0.003)\n",
    " \n",
    "fit_bnn = tfn.util.make_fit_op(\n",
    "    train_loss_bnn,\n",
    "    opt_bnn,\n",
    "    bnn.trainable_variables,\n",
    "    grad_summary_fn=lambda gs: tf.nest.map_structure(tf.norm, gs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "LS8nQqN3FMFv"
   },
   "outputs": [],
   "source": [
    "#@title Eval Helpers\n",
    "def all_categories(d):\n",
    "  num_classes = tf.shape(d.logits_parameter())[-1]\n",
    "  batch_ndims = tf.size(d.batch_shape_tensor())\n",
    "  expand_shape = tf.pad(\n",
    "      [num_classes], paddings=[[0, batch_ndims]], constant_values=1)\n",
    "  return tf.reshape(tf.range(num_classes, dtype=d.dtype), expand_shape)\n",
    "\n",
    "\n",
    "def rollaxis(x, shift):\n",
    "  return tf.transpose(x, tf.roll(tf.range(tf.rank(x)), shift=shift, axis=0))\n",
    "\n",
    "\n",
    "def compute_eval_stats(y, d, threshold=None):\n",
    "  # Assume we have evidence `x`, targets `y`, and model function `dnn`.\n",
    "\n",
    "  all_pred_log_prob = tf.math.log_softmax(d.logits, axis=-1)\n",
    "  yhat = tf.argmax(all_pred_log_prob, axis=-1)\n",
    "  pred_log_prob = tf.reduce_max(all_pred_log_prob, axis=-1)\n",
    "\n",
    "  # all_pred_log_prob = d.log_prob(all_categories(d))\n",
    "  # yhat = tf.argmax(all_pred_log_prob, axis=0)\n",
    "  # pred_log_prob = tf.reduce_max(all_pred_log_prob, axis=0)\n",
    "\n",
    "  # Alternative #1:\n",
    "  #   all_pred_log_prob = rollaxis(all_pred_log_prob, shift=-1)\n",
    "  #   pred_log_prob, yhat = tf.math.top_k(all_pred_log_prob, k=1, sorted=False)\n",
    "\n",
    "  # Alternative #2:\n",
    "  #   yhat = tf.argmax(all_pred_log_prob, axis=0)\n",
    "  #   pred_log_prob = tf.gather(rollaxis(all_pred_log_prob, shift=-1),\n",
    "  #                             yhat,\n",
    "  #                             batch_dims=len(d.batch_shape))\n",
    "\n",
    "  if threshold is not None:\n",
    "    keep = pred_log_prob > tf.math.log(threshold)\n",
    "    pred_log_prob = tf.boolean_mask(pred_log_prob, keep)\n",
    "    yhat = tf.boolean_mask(yhat, keep)\n",
    "    y = tf.boolean_mask(y, keep)\n",
    "\n",
    "  hit = tf.equal(y, tf.cast(yhat, y.dtype))\n",
    "  avg_acc = tf.reduce_mean(tf.cast(hit, tf.float32), axis=-1)\n",
    "\n",
    "  num_buckets = 10\n",
    "  (\n",
    "    avg_calibration_error,\n",
    "    acc,\n",
    "    conf,\n",
    "    cnt,\n",
    "    edges,\n",
    "    bucket,\n",
    "  ) = tf.cond(tf.size(y) > 0,\n",
    "              lambda: tfp.stats.expected_calibration_error_quantiles(\n",
    "                  hit,\n",
    "                  pred_log_prob,\n",
    "                  num_buckets=num_buckets,\n",
    "                  log_space_buckets=True),\n",
    "              lambda: (tf.constant(np.nan),\n",
    "                       tf.fill([num_buckets], np.nan),\n",
    "                       tf.fill([num_buckets], np.nan),\n",
    "                       tf.fill([num_buckets], np.nan),\n",
    "                       tf.fill([num_buckets + 1], np.nan),\n",
    "                       tf.constant([], tf.int64)))\n",
    "  return avg_acc, avg_calibration_error, (acc, conf, cnt, edges, bucket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "code",
    "colab": {},
    "colab_type": "code",
    "id": "iUmS-7IATIcI"
   },
   "outputs": [],
   "source": [
    "eval_iter_bnn = iter(eval_dataset.batch(2000).repeat())\n",
    "\n",
    "@tfn.util.tfcompile\n",
    "def eval_bnn(threshold=None, num_inferences=5):\n",
    "  x, y = next(eval_iter_bnn)\n",
    "  loss, (nll, kl), d = compute_loss_bnn(x, y, is_eval=True)\n",
    "  if num_inferences > 1:\n",
    "    before_avg_predicted_log_probs = tf.map_fn(\n",
    "      lambda _: tf.math.log_softmax(bnn(x).logits, axis=-1),\n",
    "      elems=tf.range(num_inferences),\n",
    "      dtype=loss.dtype)\n",
    "    d = tfd.Categorical(logits=tfp.math.reduce_logmeanexp(\n",
    "        before_avg_predicted_log_probs, axis=0))\n",
    "  avg_acc, avg_calibration_error, (acc, conf, cnt, edges, bucket) = \\\n",
    "      compute_eval_stats(y, d, threshold=threshold)\n",
    "  n = tf.reduce_sum(cnt, axis=0)\n",
    "  return loss, (nll, kl, avg_acc, avg_calibration_error, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YeEZZT0uAZjn"
   },
   "source": [
    "### 5  Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CRHw0VNK_Acu"
   },
   "outputs": [],
   "source": [
    "DEBUG_MODE = False\n",
    "tf.config.experimental_run_functions_eagerly(DEBUG_MODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "code",
    "colab": {
     "height": 900
    },
    "colab_type": "code",
    "id": "ba5W_N6oTNbo",
    "outputId": "9ce96336-071a-4878-e1ae-37684c61c60a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it:    1   ms/it:5862.5939   tst_acc:0.0845   tst_ece:0.2995   tst_tot:2000.0   trn_loss:58.9386   tst_loss:59.8447   tst_nll:6.1577   tst_kl:53.6870   sum_norm_grad:20.2655\n",
      "it:  401   ms/it:4.3082   tst_acc:0.7235   tst_ece:0.0819   tst_tot:2000.0   trn_loss:42.1540   tst_loss:42.5749   tst_nll:1.1286   tst_kl:41.4463   sum_norm_grad:5.1660\n",
      "it:  801   ms/it:4.2911   tst_acc:0.7160   tst_ece:0.0800   tst_tot:2000.0   trn_loss:31.7853   tst_loss:32.0219   tst_nll:1.8001   tst_kl:30.2218   sum_norm_grad:8.0951\n",
      "it: 1201   ms/it:4.1917   tst_acc:0.7480   tst_ece:0.0325   tst_tot:2000.0   trn_loss:24.9088   tst_loss:24.4505   tst_nll:2.4974   tst_kl:21.9532   sum_norm_grad:9.7074\n",
      "it: 1601   ms/it:3.9633   tst_acc:0.7250   tst_ece:0.0379   tst_tot:2000.0   trn_loss:18.6694   tst_loss:21.0958   tst_nll:3.5440   tst_kl:17.5518   sum_norm_grad:5.8497\n",
      "it: 2001   ms/it:3.9557   tst_acc:0.7295   tst_ece:0.0424   tst_tot:2000.0   trn_loss:20.0331   tst_loss:19.6677   tst_nll:3.9847   tst_kl:15.6830   sum_norm_grad:10.8527\n",
      "it: 2401   ms/it:4.0308   tst_acc:0.7480   tst_ece:0.0351   tst_tot:2000.0   trn_loss:20.4069   tst_loss:18.8401   tst_nll:4.1173   tst_kl:14.7227   sum_norm_grad:14.0905\n",
      "it: 2801   ms/it:3.9458   tst_acc:0.7640   tst_ece:0.0373   tst_tot:2000.0   trn_loss:17.2355   tst_loss:18.3699   tst_nll:4.2635   tst_kl:14.1065   sum_norm_grad:10.2847\n",
      "it: 3201   ms/it:3.9963   tst_acc:0.7720   tst_ece:0.0393   tst_tot:2000.0   trn_loss:16.3210   tst_loss:17.9160   tst_nll:4.2075   tst_kl:13.7085   sum_norm_grad:8.0039\n",
      "it: 3601   ms/it:4.0475   tst_acc:0.7435   tst_ece:0.0518   tst_tot:2000.0   trn_loss:18.3554   tst_loss:17.7926   tst_nll:4.3821   tst_kl:13.4104   sum_norm_grad:15.8831\n",
      "it: 4001   ms/it:4.0448   tst_acc:0.7725   tst_ece:0.0391   tst_tot:2000.0   trn_loss:18.6708   tst_loss:17.0435   tst_nll:3.8955   tst_kl:13.1480   sum_norm_grad:12.3830\n",
      "it: 4401   ms/it:4.0336   tst_acc:0.7800   tst_ece:0.0350   tst_tot:2000.0   trn_loss:16.4662   tst_loss:16.8649   tst_nll:3.8997   tst_kl:12.9652   sum_norm_grad:8.5103\n",
      "it: 4801   ms/it:4.0554   tst_acc:0.7700   tst_ece:0.0439   tst_tot:2000.0   trn_loss:16.6951   tst_loss:16.9580   tst_nll:4.1651   tst_kl:12.7929   sum_norm_grad:14.5428\n",
      "it: 5201   ms/it:3.9713   tst_acc:0.7855   tst_ece:0.0462   tst_tot:2000.0   trn_loss:16.3360   tst_loss:16.5773   tst_nll:3.9680   tst_kl:12.6094   sum_norm_grad:14.0063\n",
      "it: 5601   ms/it:4.0391   tst_acc:0.7540   tst_ece:0.0510   tst_tot:2000.0   trn_loss:19.8667   tst_loss:17.1445   tst_nll:4.7008   tst_kl:12.4437   sum_norm_grad:13.9373\n",
      "it: 6001   ms/it:3.9563   tst_acc:0.7880   tst_ece:0.0340   tst_tot:2000.0   trn_loss:14.9029   tst_loss:16.2793   tst_nll:3.9032   tst_kl:12.3761   sum_norm_grad:12.0636\n",
      "it: 6401   ms/it:3.9199   tst_acc:0.7765   tst_ece:0.0509   tst_tot:2000.0   trn_loss:17.3830   tst_loss:16.8494   tst_nll:4.5856   tst_kl:12.2638   sum_norm_grad:10.2933\n",
      "it: 6801   ms/it:4.0739   tst_acc:0.7775   tst_ece:0.0304   tst_tot:2000.0   trn_loss:13.6050   tst_loss:16.7315   tst_nll:4.5504   tst_kl:12.1811   sum_norm_grad:11.9535\n",
      "it: 7201   ms/it:3.9251   tst_acc:0.7630   tst_ece:0.0454   tst_tot:2000.0   trn_loss:14.1616   tst_loss:16.5367   tst_nll:4.4274   tst_kl:12.1093   sum_norm_grad:9.2998\n",
      "it: 7601   ms/it:4.0371   tst_acc:0.7620   tst_ece:0.0560   tst_tot:2000.0   trn_loss:13.9738   tst_loss:16.8960   tst_nll:4.8912   tst_kl:12.0047   sum_norm_grad:8.8868\n",
      "it: 8001   ms/it:4.0471   tst_acc:0.7965   tst_ece:0.0432   tst_tot:2000.0   trn_loss:13.5997   tst_loss:15.8973   tst_nll:3.9653   tst_kl:11.9320   sum_norm_grad:8.8158\n",
      "it: 8401   ms/it:3.9533   tst_acc:0.7835   tst_ece:0.0554   tst_tot:2000.0   trn_loss:13.1587   tst_loss:16.4230   tst_nll:4.5184   tst_kl:11.9045   sum_norm_grad:11.2574\n",
      "it: 8801   ms/it:4.0419   tst_acc:0.7785   tst_ece:0.0463   tst_tot:2000.0   trn_loss:12.2477   tst_loss:16.3890   tst_nll:4.5473   tst_kl:11.8417   sum_norm_grad:6.5455\n",
      "it: 9201   ms/it:4.0679   tst_acc:0.7850   tst_ece:0.0343   tst_tot:2000.0   trn_loss:16.1490   tst_loss:16.2382   tst_nll:4.4720   tst_kl:11.7663   sum_norm_grad:17.8742\n",
      "it: 9601   ms/it:3.9811   tst_acc:0.7865   tst_ece:0.0576   tst_tot:2000.0   trn_loss:14.3236   tst_loss:16.6025   tst_nll:4.9027   tst_kl:11.6998   sum_norm_grad:9.5053\n",
      "it:10001   ms/it:3.9252   tst_acc:0.7980   tst_ece:0.0409   tst_tot:2000.0   trn_loss:15.5901   tst_loss:16.3176   tst_nll:4.6021   tst_kl:11.7156   sum_norm_grad:17.3674\n",
      "it:10401   ms/it:4.0147   tst_acc:0.7865   tst_ece:0.0396   tst_tot:2000.0   trn_loss:14.1774   tst_loss:16.3389   tst_nll:4.7103   tst_kl:11.6285   sum_norm_grad:10.5411\n",
      "it:10801   ms/it:4.0667   tst_acc:0.7910   tst_ece:0.0395   tst_tot:2000.0   trn_loss:13.4732   tst_loss:15.9686   tst_nll:4.3783   tst_kl:11.5903   sum_norm_grad:8.1506\n",
      "it:11201   ms/it:4.0664   tst_acc:0.7905   tst_ece:0.0429   tst_tot:2000.0   trn_loss:13.6723   tst_loss:16.0677   tst_nll:4.4813   tst_kl:11.5864   sum_norm_grad:15.1697\n",
      "it:11601   ms/it:3.9268   tst_acc:0.7830   tst_ece:0.0541   tst_tot:2000.0   trn_loss:15.6687   tst_loss:16.3242   tst_nll:4.7401   tst_kl:11.5842   sum_norm_grad:9.7051\n",
      "it:12001   ms/it:3.9141   tst_acc:0.7740   tst_ece:0.0599   tst_tot:2000.0   trn_loss:13.1158   tst_loss:15.9077   tst_nll:4.4175   tst_kl:11.4901   sum_norm_grad:7.5720\n",
      "it:12401   ms/it:3.9359   tst_acc:0.7810   tst_ece:0.0489   tst_tot:2000.0   trn_loss:14.0706   tst_loss:16.4156   tst_nll:4.9192   tst_kl:11.4964   sum_norm_grad:11.2685\n",
      "it:12801   ms/it:3.9897   tst_acc:0.7905   tst_ece:0.0448   tst_tot:2000.0   trn_loss:12.5457   tst_loss:16.3654   tst_nll:4.9135   tst_kl:11.4519   sum_norm_grad:6.0437\n",
      "it:13201   ms/it:4.0318   tst_acc:0.7925   tst_ece:0.0395   tst_tot:2000.0   trn_loss:13.2101   tst_loss:15.9976   tst_nll:4.5442   tst_kl:11.4534   sum_norm_grad:9.0581\n",
      "it:13601   ms/it:3.9460   tst_acc:0.7725   tst_ece:0.0631   tst_tot:2000.0   trn_loss:13.7644   tst_loss:16.4324   tst_nll:5.0305   tst_kl:11.4019   sum_norm_grad:12.7517\n",
      "it:14001   ms/it:4.0203   tst_acc:0.7845   tst_ece:0.0534   tst_tot:2000.0   trn_loss:12.6561   tst_loss:15.9307   tst_nll:4.5949   tst_kl:11.3358   sum_norm_grad:7.3136\n",
      "it:14401   ms/it:3.9536   tst_acc:0.7825   tst_ece:0.0505   tst_tot:2000.0   trn_loss:16.6286   tst_loss:16.1967   tst_nll:4.8534   tst_kl:11.3433   sum_norm_grad:13.0344\n",
      "it:14801   ms/it:3.9800   tst_acc:0.7805   tst_ece:0.0519   tst_tot:2000.0   trn_loss:15.4574   tst_loss:15.9306   tst_nll:4.5774   tst_kl:11.3532   sum_norm_grad:11.1836\n",
      "it:15201   ms/it:3.9686   tst_acc:0.8005   tst_ece:0.0473   tst_tot:2000.0   trn_loss:14.6427   tst_loss:15.7785   tst_nll:4.4554   tst_kl:11.3232   sum_norm_grad:8.3436\n",
      "it:15601   ms/it:3.9500   tst_acc:0.7845   tst_ece:0.0657   tst_tot:2000.0   trn_loss:14.7354   tst_loss:15.9813   tst_nll:4.7060   tst_kl:11.2753   sum_norm_grad:11.7336\n",
      "it:16001   ms/it:4.0056   tst_acc:0.7825   tst_ece:0.0611   tst_tot:2000.0   trn_loss:15.5257   tst_loss:15.7183   tst_nll:4.4438   tst_kl:11.2745   sum_norm_grad:14.9541\n",
      "it:16401   ms/it:3.9944   tst_acc:0.7925   tst_ece:0.0436   tst_tot:2000.0   trn_loss:11.4951   tst_loss:16.1987   tst_nll:4.9474   tst_kl:11.2513   sum_norm_grad:3.4657\n",
      "it:16801   ms/it:3.9387   tst_acc:0.7985   tst_ece:0.0466   tst_tot:2000.0   trn_loss:13.3886   tst_loss:16.4558   tst_nll:5.2083   tst_kl:11.2475   sum_norm_grad:7.6735\n",
      "it:17201   ms/it:4.0567   tst_acc:0.8035   tst_ece:0.0453   tst_tot:2000.0   trn_loss:12.8670   tst_loss:15.6634   tst_nll:4.4216   tst_kl:11.2417   sum_norm_grad:8.6170\n",
      "it:17601   ms/it:4.0974   tst_acc:0.7740   tst_ece:0.0529   tst_tot:2000.0   trn_loss:12.9106   tst_loss:16.2054   tst_nll:5.0212   tst_kl:11.1842   sum_norm_grad:7.1696\n",
      "it:18001   ms/it:3.9423   tst_acc:0.7860   tst_ece:0.0629   tst_tot:2000.0   trn_loss:12.9261   tst_loss:15.6393   tst_nll:4.4449   tst_kl:11.1944   sum_norm_grad:13.0945\n",
      "it:18401   ms/it:3.9851   tst_acc:0.8025   tst_ece:0.0416   tst_tot:2000.0   trn_loss:11.8293   tst_loss:15.8313   tst_nll:4.6622   tst_kl:11.1691   sum_norm_grad:3.6171\n",
      "it:18801   ms/it:4.0366   tst_acc:0.7925   tst_ece:0.0613   tst_tot:2000.0   trn_loss:13.4378   tst_loss:16.2667   tst_nll:5.0887   tst_kl:11.1779   sum_norm_grad:9.5677\n",
      "it:19201   ms/it:3.9272   tst_acc:0.7925   tst_ece:0.0433   tst_tot:2000.0   trn_loss:15.4271   tst_loss:16.1166   tst_nll:4.9649   tst_kl:11.1517   sum_norm_grad:11.7118\n",
      "it:19601   ms/it:3.9611   tst_acc:0.7540   tst_ece:0.0734   tst_tot:2000.0   trn_loss:11.9052   tst_loss:16.3360   tst_nll:5.1913   tst_kl:11.1447   sum_norm_grad:8.8169\n",
      "it:20000   ms/it:3.9542   tst_acc:0.7970   tst_ece:0.0472   tst_tot:2000.0   trn_loss:11.8701   tst_loss:15.4936   tst_nll:4.3523   tst_kl:11.1413   sum_norm_grad:7.0496\n"
     ]
    }
   ],
   "source": [
    "num_train_epochs = 2.  # @param { isTemplate: true}\n",
    "num_evals = 50         # @param { isTemplate: true\n",
    "\n",
    "dur_sec = dur_num = 0\n",
    "num_train_steps = int(num_train_epochs * train_size)\n",
    "for i in range(num_train_steps):\n",
    "  start = time.time()\n",
    "  trn_loss, (trn_nll, trn_kl), g = fit_bnn()\n",
    "  stop = time.time()\n",
    "  dur_sec += stop - start\n",
    "  dur_num += 1\n",
    "  if i % int(num_train_steps / num_evals) == 0 or i == num_train_steps - 1:\n",
    "    tst_loss, (tst_nll, tst_kl, tst_acc, tst_ece, tst_tot) = eval_bnn()\n",
    "    f, x = zip(*[\n",
    "        ('it:{:5}', opt_bnn.iterations),\n",
    "        ('ms/it:{:6.4f}', dur_sec / max(1., dur_num) * 1000.),\n",
    "        ('tst_acc:{:6.4f}', tst_acc),\n",
    "        ('tst_ece:{:6.4f}', tst_ece),\n",
    "        ('tst_tot:{:5}', tst_tot),\n",
    "        ('trn_loss:{:6.4f}', trn_loss),\n",
    "        ('tst_loss:{:6.4f}', tst_loss),\n",
    "        ('tst_nll:{:6.4f}', tst_nll),\n",
    "        ('tst_kl:{:6.4f}', tst_kl),\n",
    "        ('sum_norm_grad:{:6.4f}', sum(g)),\n",
    "\n",
    "    ])\n",
    "    print('   '.join(f).format(*[getattr(x_, 'numpy', lambda: x_)()\n",
    "                                 for x_ in x]))\n",
    "    sys.stdout.flush()\n",
    "    dur_sec = dur_num = 0\n",
    "  # if i % 1000 == 0 or i == maxiter - 1:\n",
    "  #   bnn.save('/tmp/bnn.npz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G1ImqkK7xAv1"
   },
   "source": [
    "### 6  Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "Wt5fjxTLfFUo"
   },
   "outputs": [],
   "source": [
    "#@title More Eval Helpers\n",
    "@tfn.util.tfcompile\n",
    "def compute_log_probs_bnn(x, num_inferences):\n",
    "  lp = tf.map_fn(lambda _: tf.math.log_softmax(bnn_eval(x).logits, axis=-1),\n",
    "                 elems=tf.range(num_inferences),\n",
    "                 dtype=tf.float32)\n",
    "  log_mean_prob = tfp.math.reduce_logmeanexp(lp, axis=0)\n",
    "  # ovr = \"one vs rest\"\n",
    "  log_avg_std_ovr_prob = tfp.math.reduce_logmeanexp(lp + tf.math.log1p(-lp), axis=0)\n",
    "  #log_std_prob = 0.5 * tfp.math.log_sub_exp(log_mean2_prob, log_mean_prob * 2.)\n",
    "  tiny_ = np.finfo(lp.dtype.as_numpy_dtype).tiny\n",
    "  log_std_prob = 0.5 * tfp.math.reduce_logmeanexp(\n",
    "      2 * tfp.math.log_sub_exp(lp + tiny_, log_mean_prob),\n",
    "      axis=0)\n",
    "  return log_mean_prob, log_std_prob, log_avg_std_ovr_prob \n",
    "\n",
    "num_inferences = 50\n",
    "num_chunks = 10\n",
    "\n",
    "eval_iter_bnn = iter(eval_dataset.batch(eval_size // num_chunks))\n",
    "\n",
    "@tfn.util.tfcompile\n",
    "def all_eval_labels_and_log_probs_bnn():\n",
    "  def _inner(_):\n",
    "    x, y = next(eval_iter_bnn)\n",
    "    return x, y, compute_log_probs_bnn(x, num_inferences)\n",
    "  x, y, (log_probs, log_std_probs, log_avg_std_ovr_prob) = tf.map_fn(\n",
    "      _inner,\n",
    "      elems=tf.range(num_chunks),\n",
    "      dtype=(tf.float32, tf.int32,) + ((tf.float32,) * 3,))\n",
    "  return (\n",
    "      tf.reshape(x, (-1,) + image_shape),\n",
    "      tf.reshape(y, [-1]),\n",
    "      tf.reshape(log_probs, [-1, num_classes]),\n",
    "      tf.reshape(log_std_probs, [-1, num_classes]),\n",
    "      tf.reshape(log_avg_std_ovr_prob, [-1, num_classes]),\n",
    "  )\n",
    "\n",
    "(\n",
    "   x_, y_,\n",
    "   log_probs_, log_std_probs_,\n",
    "   log_avg_std_ovr_prob_,\n",
    ") = all_eval_labels_and_log_probs_bnn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "c65_RnFIwruI"
   },
   "outputs": [],
   "source": [
    "#@title Run Eval\n",
    "x, y, log_probs, log_std_probs, log_avg_std_ovr_prob = (\n",
    "    x_, y_, log_probs_, log_std_probs_, log_avg_std_ovr_prob_)\n",
    "\n",
    "yhat = tf.argmax(log_probs, axis=-1)\n",
    "max_log_probs = tf.gather(log_probs, yhat, batch_dims=1)\n",
    "max_log_std_probs = tf.gather(log_std_probs, yhat, batch_dims=1)\n",
    "max_log_avg_std_ovr_prob = tf.gather(log_avg_std_ovr_prob, yhat, batch_dims=1)\n",
    "\n",
    "# Sort by ascending confidence.\n",
    "score = max_log_probs                       # Mean\n",
    "#score = -max_log_std_probs                 # 1 / Sigma\n",
    "#score = max_log_probs - max_log_std_probs  # Mean / Sigma\n",
    "#score = abs(tf.math.expm1(max_log_std_probs - (max_log_probs + tf.math.log1p(-max_log_probs))))\n",
    "\n",
    "idx = tf.argsort(score)\n",
    "score = tf.gather(score, idx)\n",
    "x = tf.gather(x, idx)\n",
    "y = tf.gather(y, idx)\n",
    "yhat = tf.gather(yhat, idx)\n",
    "hit = tf.cast(tf.equal(y, tf.cast(yhat,y.dtype)), tf.int32)\n",
    "log_probs = tf.gather(log_probs, idx)\n",
    "max_log_probs = tf.gather(max_log_probs, idx)\n",
    "log_std_probs = tf.gather(log_std_probs, idx)\n",
    "max_log_std_probs = tf.gather(max_log_std_probs, idx)\n",
    "log_avg_std_ovr_prob = tf.gather(log_avg_std_ovr_prob, idx)\n",
    "max_log_avg_std_ovr_prob = tf.gather(max_log_avg_std_ovr_prob, idx)\n",
    "\n",
    "d = tfd.Categorical(logits=log_probs)\n",
    "\n",
    "max_log_probs = tf.reduce_max(log_probs, axis=-1)\n",
    "\n",
    "keep = tf.range(500,eval_size)\n",
    "#threshold = 0.95;\n",
    "# keep = tf.where(max_log_probs > tf.math.log(threshold))[..., 0]\n",
    "\n",
    "x_keep = tf.gather(x, keep)\n",
    "y_keep = tf.gather(y, keep)\n",
    "log_probs_keep = tf.gather(log_probs, keep)\n",
    "yhat_keep = tf.gather(yhat, keep)\n",
    "d_keep = tfd.Categorical(logits=log_probs_keep)\n",
    "\n",
    "(\n",
    "    avg_acc, ece,\n",
    "    (acc, conf, cnt, edges, bucket),\n",
    ") = tfn.util.tfcompile(lambda: compute_eval_stats(y, d))()\n",
    "\n",
    "(\n",
    "    avg_acc_keep, ece_keep,\n",
    "    (acc_keep, conf_keep, cnt_keep, edges_keep, bucket_keep),\n",
    ") = tfn.util.tfcompile(lambda: compute_eval_stats(y_keep, d_keep))()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 101
    },
    "colab_type": "code",
    "id": "53J7TS0mim_A",
    "outputId": "82665715-6ecd-4628-fa76-4617f22ad11d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurary (all)     : 0.8123999834060669\n",
      "Accurary (certain) : 0.8370526432991028\n",
      "ECE      (all)     : 0.015362780541181564\n",
      "ECE      (certain) : 0.01939529925584793\n",
      "Number undecided: 500\n"
     ]
    }
   ],
   "source": [
    "print('Accurary (all)     : {}'.format(avg_acc))\n",
    "print('Accurary (certain) : {}'.format(avg_acc_keep))\n",
    "print('ECE      (all)     : {}'.format(ece))\n",
    "print('ECE      (certain) : {}'.format(ece_keep))\n",
    "print('Number undecided: {}'.format(eval_size - tf.size(keep)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 675
    },
    "colab_type": "code",
    "id": "b_atF5vNiqJ2",
    "outputId": "58c6af3d-d46a-462e-8b7d-748cfb5e04f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most uncertain:\n"
     ]
    },
    {
     "data": {
      "image/png": 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QaDSyVN3KmJqaIjQ0tF5U0BkzZsDFxUXW+QWAuLg4zJkzR1pOdMaMGYr2f/36dUn5ZBjm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MxV9BoFVMXt2GOPBWDatGlA3vmqvKkSqES99tprST3Va6OyYV9kd7TuBqxDpyLy+uuvJ99NTzDv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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 1 0 0 0]\n",
      " [0 0 1 0 0 1 1 0 0 0 0 1]\n",
      " [0 0 0 0 0 0 0 0 1 0 0 0]\n",
      " [1 1 0 0 0 0 0 0 0 1 0 0]\n",
      " [0 1 0 0 1 0 0 0 0 0 0 1]\n",
      " [0 0 0 0 0 1 0 1 0 0 0 0]]\n",
      "[['8' 'W' 'y' 'K' '0' 'z' 'w' 'P' 'O' 'e' 'k' '2']\n",
      " ['P' 'a' 'x' 'L' 'H' 'S' 'R' 'g' 'e' 'e' '1' 'C']\n",
      " ['0' '2' 'V' '2' '8' 'V' 'A' 'W' 'Q' '0' 'x' '1']\n",
      " ['1' 'e' 'U' 'T' 'z' 'L' 'C' 'z' 'h' 'v' '7' 'F']\n",
      " ['e' 'T' '2' 'n' 'd' 'z' '5' 'n' 'z' 'O' 'I' 'A']\n",
      " ['N' 'p' 'I' 'w' 'V' 'A' 'q' '3' 't' 'z' '8' 'r']]\n"
     ]
    }
   ],
   "source": [
    "print('Most uncertain:')\n",
    "ss = (6,12); n = np.prod(ss); s = ss+image_shape\n",
    "tfn.util.display_imgs(\n",
    "    tf.reshape(x[:n], s),\n",
    "    yhuman[tf.reshape(y[:n], ss).numpy()])\n",
    "print(tf.reshape(hit[:n], ss).numpy())\n",
    "print(yhuman[tf.reshape(yhat[:n], ss).numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 675
    },
    "colab_type": "code",
    "id": "Znmom4_visE9",
    "outputId": "0ebae465-5162-413d-908b-242e245a6df4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Least uncertain:\n"
     ]
    },
    {
     "data": {
      "image/png": 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RlZWFl19+GaGhoYiNjTVkw1Hl0717dxw5cgQLFy6E1WqF1WrF7t27DS95b9q0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\npvOMJQ1njj322EOSH+M999zT3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Prppz+LDOY4YB6ceOKJLh4QwvamxLWhSJDJKfm6cnwH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\nGfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]]\n",
      "[['9' '3' '2' '7' '8' '5' '3' '2' '4' '4' '3' '7']\n",
      " ['3' 't' '7' 'e' '2' '6' '2' '7' 't' '2' 'e' '9']\n",
      " ['8' '5' '8' '3' '3' '8' '8' '6' '8' '7' '9' 'r']\n",
      " ['8' '3' '4' '9' '8' 'e' '4' '6' '3' '8' 'r' 'd']\n",
      " ['9' 'r' '7' '2' '5' '6' '3' '8' '3' 'b' '7' '6']\n",
      " ['3' '5' '3' '8' '5' '3' '6' '6' '9' '8' '3' '6']]\n"
     ]
    }
   ],
   "source": [
    "print('Least uncertain:')\n",
    "tfn.util.display_imgs(\n",
    "    tf.reshape(x[-n:], s),\n",
    "    yhuman[tf.reshape(y[-n:], ss).numpy()])\n",
    "print(tf.reshape(hit[-n:], ss).numpy())\n",
    "print(yhuman[tf.reshape(yhat[-n:], ss).numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 283
    },
    "colab_type": "code",
    "id": "4nDO74UmithH",
    "outputId": "47687a04-52ad-41fe-b257-01f06b3271bf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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daksiV9vs4kmNWRhbz9fYiaah6muussZpja16P1B5hZGPt10Zumwwmh0SQm3D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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = tf.math.exp(max_log_probs)\n",
    "b = tf.math.exp(max_log_std_probs)\n",
    "plt.plot(a, b, '.', label='observed');\n",
    "#sns.jointplot(a.numpy(), b.numpy())\n",
    "\n",
    "plt.xlabel('mean');\n",
    "plt.ylabel('std');\n",
    "p = tf.linspace(0.,1,100)\n",
    "plt.plot(p, tf.math.sqrt(p * (1 - p)), label='theoretical');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 283
    },
    "colab_type": "code",
    "id": "lD0U-zA3bRN7",
    "outputId": "61952d6a-5622-480f-da68-43d5ca587e21"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j4xEXF4dVq1bd9LogCHjllVcQFxeHYcOGIScnR4Iq792d+rl+/XoMGzYMw4YN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\njkFARCRz/x/8OOTdtVkO4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = max_log_probs\n",
    "# b = tf.boolean_mask(b, b < 0.)\n",
    "sns.distplot(tf.math.exp(b).numpy(), bins=20);\n",
    "plt.xlabel('Posterior Mean Pred Prob');\n",
    "plt.ylabel('Freq');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 283
    },
    "colab_type": "code",
    "id": "pdWqz85HrqW5",
    "outputId": "bb773a3e-3dc0-449e-f3a2-043342f2ad74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fHG2t8fHyFLOGPZExnO1scE24B745VjXit1+9EskCf+nSpfD29kZsbKxUTZhE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\nvPuJUojOIoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = max_log_std_probs\n",
    "tiny_ = np.finfo(b.dtype.as_numpy_dtype).tiny\n",
    "b = tf.boolean_mask(b, b > tf.math.log(tiny_))\n",
    "sns.distplot(tf.math.exp(b).numpy(), bins=20);\n",
    "plt.xlabel('Posterior Std. Pred Prob');\n",
    "plt.ylabel('Freq');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 283
    },
    "colab_type": "code",
    "id": "OBmjOrHOcky6",
    "outputId": "525fca08-7303-44e3-b61a-1722966454d0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aueQ+gttvvx233367K5rudaRw+asD97XvktL+9sV9lYmuem+JiEgyOK6QiEji\nGARO8PXXXyM+Ph5DhgzBypUrL/v8n//8J4YPH47hw4fjpptuwokTJ1xQZc+51v52+PHHH6FQKPDp\np586sbqeZc++7t+/H1qtFklJSZg0aZKTK+w519rX+vp63HnnnRgxYgSSkpKwbt06F1TZM5YuXYrQ\n0FAkJ3f9GEkhBP7whz9gyJAhGD58OI4ePerkCnuYIIcymUwiNjZW5Ofni9bWVjF8+HDxyy+/dFrm\n0KFDoqamRgghxI4dO8SYMWNcUWqPsGd/O5abPHmymDlzptiyZYsLKr1x9uxrbW2tSExMFEVFRUII\nIcrLy11R6g2zZ1//+7//Wzz77LNCCCEqKipEUFCQaG1tdUW5N+w///mPOHLkiEhKSury8y+//FLM\nmDFDWCwW8d1337n1/1khhOAZgYNdOqWGSqWyTalxqZtuuglBQUEAgHHjxqG4uNgVpfYIe/YXAN58\n803MmzcPoaGOvXXekezZ148++ghz585FdHQ0ALjt/tqzrzKZDI2NjRBCQK/Xo1+/flAq3XOC4/T0\ndPTr1++Kn2/fvh0PPvggZDIZxo0bh7q6OpSVlTmxwp7FIHCwrqbUKCkpueLya9euxcyZM51RmkPY\ns78lJSXYunUrHnvsMWeX16Ps2dfc3FzU1tbilltuwahRo/DBBx84u8weYc++/v73v8fp06cRERGB\nlJQUrF69GnJ53zzEdPf/dW/nnnHtRoSdU2oAwL59+7B27VocPHjQ0WU5jD37+9RTT+HVV1+FQuHe\nzyOwZ19NJhOOHDmCPXv2oLm5GePHj8e4ceMwdOhQZ5XZI+zZ1507d0Kr1WLv3r3Iz8/HtGnTMHHi\nRPj7u+apW47Unf/X7oBB4GD2Tqnx008/ITMzE1999RWCg4OdWWKPsmd/dTodFi5cCACoqqrCjh07\noFQqcffddzu11htlz75GRUUhJCQEarUaarUa6enpOHHihNsFgT37um7dOixfvhwymQxDhgxBTEwM\nzpw5gzFjxji7XIez9/+123BlB4UUGI1GERMTIwoKCmydbCdPnuy0TFFRkRg8eLA4dOiQi6rsOfbs\n76UWL17stp3F9uzrqVOnxK233iqMRqNoamoSSUlJ4ueff3ZRxdfPnn197LHHxIsvviiEEOLixYsi\nIiJCVFZWuqDannHu3LkrdhZ/8cUXnTqLR48e7eTqehbPCBzsSlNqvPvuuwCAxx57DC+99BKqq6vx\nu9/9zraOu05qZc/+9hX27GtiYiJmzJiB4cOHQy6XIzMz84pDEnsze/b1L3/5C5YsWYKUlBQIIfDq\nq68iJKS3z//btYyMDOzfvx/brVZ7AAAKqElEQVRVVVWIiorCihUrYDRaH+T02GOP4fbbb8eOHTsw\nZMgQ+Pj4uPVQWYB3FhMRSV7f7NInIiK7MQiIiCSOQUBEJHEMAiIiiWMQEBFJHINAQhQKBbRaLZKT\nkzF//nwYDIZub+ONN964rvVeeOEF7N69u9vr/daIESOQkZFxw9vpYDAYcP/99yMlJQXJycmYMGEC\n9Ho96urq8M4771xxvSVLllxz1tT169dDo9FAq9Vi2LBheP/992+o1iu1uWTJEsTExECr1SI1NRXf\nffddj2z3t8rKynDHHXfYfj548CDGjBmDhIQEJCQkIDs7G4B1ttXx48d3WtdkMiEsLAxlZWWd6h0x\nYgT27NljW27hwoXIy8vrVv104xgEEuLt7Y3jx4/j5MmTUKlUtjHg3XE9QWA2m/HSSy9h6tSp3Vrn\nt06fPg2LxYIDBw6gqampWzVcyerVqxEWFoaff/4ZJ0+exNq1a+Hh4XHNILDXggULcPz4cezfvx/P\nPfccysvLO31uMpluuA0AeO2113D8+HGsXLkSjz766GWf90Q7f//73/HII48AAC5evIj77rsP7777\nLs6cOYODBw/ivffew5dffon09HQUFxejsLDQtu7u3buRnJyM8PDwTvW+8cYbne4tefzxx7Fq1aob\nrpW6h0EgURMnTsTZs2cBWP+DJycnIzk5GW+88QYAoKmpCbNmzcKIESOQnJyMzZs3Y82aNSgtLcXk\nyZMxefJkAMC///1vjB8/HqmpqZg/fz70ej0AYNCgQXjppZcwYcIEbNmypdO3zj179mDkyJFISUnB\n0qVL0dra2uU6v/XRRx9h0aJFuO222/D5558DsIbDpVMYFBYWYvjw4QCAHTt2ICEhARMmTMAf/vCH\nTt9mO5SVlSEyMtL2c3x8PDw9PbF8+XLk5+dDq9Vi2bJlEELg97//PYYNG4ZZs2ahoqKiW7/v0NBQ\nDB48GEVFRfjrX/+KrKws3HbbbXjwwQdhNpuxbNkyjB49GsOHD8d7770HANfVZnp6uu3f9ZZbbsFz\nzz2HSZMmYfXq1SgqKsKUKVMwfPhwTJkyBefPn7ett3v3bkycOBFDhw7FF1980eW2P/vsM8yYMQMA\n8Pbbb2PJkiVITU0FAISEhGDVqlVYuXIl5HI55s+fj82bN9vW3bRpU5dncuPHj+80WdvEiROxe/fu\nHgtIspNL72smp1Kr1UII63QBs2fPFu+8847Q6XQiOTlZ6PV60djYKIYNGyaOHj0qPv30U5GZmWlb\nt66uTgghxMCBA23TBlRWVoqJEycKvV4vhBBi5cqVYsWKFbblXn31Vdv6HVNJNDc3i6ioKJGTkyOE\nEGLRokXi9ddf73Kd34qLixOFhYVi586d4s4777S9P2LECJGfn2+r4eWXX7a1U1BQIIQQYuHChWLW\nrFmXbfPYsWNCo9GIcePGieeff17k5uYKIS6fXuCzzz4TU6dOFSaTSZSUlIiAgIBrTo2xbt068cQT\nTwghhMjPzxcajUZUV1eLF198UaSmpgqDwSCEEOK9994TL7/8shBCiJaWFjFq1ChRUFBgd5uXTtPx\nySef2ObGnzRpknj88cdty91xxx1i/fr1Qggh1q5dK+666y7b+tOnTxdms1nk5uaKyMhI0dzc3KmN\ngoICkZqaavt5zpw5Ytu2bZ2WqaurE0FBQUIIIX744Qeh1Wpt+6TRaGzP3Li03q1bt4qMjIxO25k6\ndarQ6XRX/d1Sz+IZgYQ0NzdDq9UiLS0N0dHRePjhh3Hw4EHMmTMHarUavr6+mDt3Lr755hukpKRg\n9+7d+NOf/oRvvvkGAQEBl23v8OHDOHXqFG6++WZotVps2LABRUVFts8XLFhw2To5OTmIiYmxTbq2\nePFiHDhw4KrrANanmWk0GgwcOBBTpkzB0aNHUVtbCwC499578cknnwAANm/ejAULFuDMmTOIjY1F\nTEwMAFyxX0Gr1aKgoADLli1DTU0NRo8ejdOnT1+23IEDB5CRkQGFQoGIiAjceuutXW7vtzZv3gyt\nVouMjAy89957tjnuZ8+eDW9vbwDWs6oPPvgAWq0WY8eORXV1NfLy8rrV5rJly6DVapGdnY21a9fa\n3r/09/ndd9/hvvvuAwAsWrSo0yy39957L+RyOeLi4hAbG4szZ8502n5ZWRk0Go3tZyFEl7Ntdrw3\nevRo6PV65OTk4KuvvsK4ceNsz9zoqDc2NhYPPPAAnnvuuU7bCA0NRWlp6RX3lXoe5xqSkI4+gkuJ\nK8wwMnToUBw5cgQ7duzAn//8Z9x222144YUXLlt32rRp+Pjjj7vchlqtvuy9K7V3tXUA4OOPP8aZ\nM2cwaNAgAEBDQwM+++wzZGZmYsGCBZg/fz7mzp0LmUyGuLg4HDt27KrtXKojAOfOnQu5XI4dO3Zg\n3rx5ly13PdMML1iwAG+99dZl71+6n0IIvPnmm5g+fXqnZXbs2GF3m6+99hruueeeq7bzW5du+7ft\n/PZnb29vtLS02H5OSkqCTqfD7Nmzbe8dOXIEw4YNs/28cOFCbNq0CadPn74siF977TXMnTsXa9as\nweLFi3HkyBHbZy0tLbaQJOfgGYHEpaenY9u2bTAYDGhqasLWrVsxceJElJaWwsfHBw888ACeeeYZ\n2zNZ/fz80NjYCMD6NLVDhw7ZrkkbDAbk5uZetb2EhAQUFhba1vnwww+v+Rxfi8WCLVu24KeffkJh\nYSEKCwuxfft2WwANHjwYCoUCL7/8su0bcEJCAgoKCmwdlpder77UoUOHbGcWbW1tOHXqFAYOHNhp\nPzt+T5s2bYLZbEZZWRn27dt31Zq7Y/r06fjHP/5hm9QsNzcXTU1NPd7mTTfdhE2bNgGwPid7woQJ\nts+2bNkCi8WC/Px8FBQUID4+vtO6Q4cO7dT5+8QTT2D9+vW2LxbV1dX405/+hGeffda2TEZGBjZu\n3Ii9e/d2CowOcrkcTz75JCwWC3bu3Gl7Pzc3F0lJSTe0r9Q9PCOQuNTUVCxZssTW4ZqZmYmRI0di\n586dWLZsGeRyOTw8PPCPf/wDAJCVlYWZM2ciPDwc+/btw/r165GRkWHr8H3llVeuOte+l5cX1q1b\nh/nz58NkMmH06NHXnJH0wIEDiIyM7NSpm56ejlOnTqGsrAzh4eFYsGABli1bhnPnzgGwfoN95513\nMGPGDISEhFxxTvz8/Hw8/vjjEELAYrFg1qxZmDdvHmQyGW6++WYkJydj5syZWLVqFfbu3YuUlBQM\nHTq0U3i98MILSEtL6/JgZ4/MzEwUFhYiNTUVQghoNBps27YNc+bMuWKb12PNmjVYunQpXnvtNWg0\nmk4zZsbHx2PSpEkoLy/Hu+++Cy8vr07rqtVqDB48GGfPnsWQIUMQHh6OjRs34pFHHrE9nvKpp57C\nnXfeaVtn2LBh8PHxwahRo654ZiKTyfBf//VfWLVqFaZPn47y8nJ4e3vbRheRc3D2Ueqz9Ho9fH19\nIYTAE088gbi4OPzxj390dVlua+vWrThy5AheeeUVh7Xx+uuvw9/fHw8//LDD2qDL8dIQ9Vnvv/8+\ntFotkpKSUF9f3+X4erLfnDlzbH00jhIYGIjFixc7tA26HM8IiIgkjmcEREQSxyAgIpI4BgERkcQx\nCIiIJI5BQEQkcQwCIiKJ+39yYgAVOnsWUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = max_log_avg_std_ovr_prob\n",
    "sns.distplot(tf.math.exp(b).numpy(), bins=20);\n",
    "plt.xlabel('Posterior Avg Std. Pred Prob (OVR)');\n",
    "plt.ylabel('Freq');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {
     "height": 51
    },
    "colab_type": "code",
    "id": "zedk947YWucS",
    "outputId": "03675a4b-443f-4539-e560-c3bf461d7c3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg per class AUC:\n",
      "0.9909210101534324\n"
     ]
    }
   ],
   "source": [
    "#@title Avg One-vs-Rest AUC\n",
    "try:\n",
    "  bnn_auc = sklearn_metrics.roc_auc_score(\n",
    "      y_keep,\n",
    "      log_probs_keep,\n",
    "      average='macro',\n",
    "      multi_class='ovr')  \n",
    "  print('Avg per class AUC:\\n{}'.format(bnn_auc))\n",
    "except TypeError:\n",
    "  bnn_auc = np.array([\n",
    "    sklearn_metrics.roc_auc_score(tf.equal(y_keep, i), log_probs_keep[:, i])\n",
    "    for i in range(num_classes)])\n",
    "  print('Avg per class AUC:\\n{}'.format(bnn_auc.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a_LR5N47a1ce"
   },
   "source": [
    "### 7 Appendix: Compare against DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 203
    },
    "colab_type": "code",
    "id": "xaE5mdj5a4Xj",
    "outputId": "21049ec8-70bc-4fdd-c0d6-bfddedce7e18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== DNN ==================================================\n",
      "  SIZE SHAPE                TRAIN NAME                                    \n",
      "     8 [8]                  True  bias:0                                  \n",
      "   200 [5, 5, 1, 8]         True  kernel:0                                \n",
      "    16 [16]                 True  bias:0                                  \n",
      "  3200 [5, 5, 8, 16]        True  kernel:0                                \n",
      "    32 [32]                 True  bias:0                                  \n",
      " 12800 [5, 5, 16, 32]       True  kernel:0                                \n",
      "    61 [61]                 True  bias:0                                  \n",
      " 95648 [1568, 61]           True  kernel:0                                \n",
      "trainable size: 111965  /  0.427 MiB  /  {float32: 111965}\n"
     ]
    }
   ],
   "source": [
    "max_pool = tf.keras.layers.MaxPooling2D(  # Has no tf.Variables.\n",
    "    pool_size=(2, 2),\n",
    "    strides=(2, 2),\n",
    "    padding='SAME',\n",
    "    data_format='channels_last')\n",
    "\n",
    "maybe_batchnorm = batchnorm(axis=[-4, -3, -2])\n",
    "# maybe_batchnorm = lambda x: x\n",
    "\n",
    "dnn = tfn.Sequential([\n",
    "  lambda x: 2. * tf.cast(x, tf.float32) - 1.,  # Center.\n",
    "  tfn.Convolution(\n",
    "      input_size=1,\n",
    "      output_size=8,\n",
    "      filter_shape=5,\n",
    "      padding='SAME',\n",
    "      init_kernel_fn=tf.initializers.he_uniform(),\n",
    "      name='conv1'),\n",
    "  maybe_batchnorm,\n",
    "  tf.nn.leaky_relu,\n",
    "  tfn.Convolution(\n",
    "      input_size=8,\n",
    "      output_size=16,\n",
    "      filter_shape=5,\n",
    "      padding='SAME',\n",
    "      init_kernel_fn=tf.initializers.he_uniform(),\n",
    "      name='conv1'),\n",
    "  maybe_batchnorm,\n",
    "  tf.nn.leaky_relu,\n",
    "  max_pool,  # [28, 28, 8] -> [14, 14, 8]\n",
    "  tfn.Convolution(\n",
    "      input_size=16,\n",
    "      output_size=32,\n",
    "      filter_shape=5,\n",
    "      padding='SAME',\n",
    "      init_kernel_fn=tf.initializers.he_uniform(),\n",
    "      name='conv2'),\n",
    "  maybe_batchnorm,\n",
    "  tf.nn.leaky_relu,\n",
    "  max_pool,  # [14, 14, 16] -> [7, 7, 16]\n",
    "  tfn.util.flatten_rightmost(ndims=3),\n",
    "  tfn.Affine(\n",
    "      input_size=7 * 7 * 32,\n",
    "      output_size=num_classes - 1,\n",
    "      name='affine1'),\n",
    "  tfb.Pad(),\n",
    "  lambda x: tfd.Categorical(logits=x, dtype=tf.int32),   \n",
    "], name='DNN')\n",
    "\n",
    "# dnn_eval = tfn.Sequential([l for l in dnn.layers if l is not maybe_batchnorm],\n",
    "#                           name='dnn_eval')\n",
    "dnn_eval = dnn\n",
    "\n",
    "print(dnn.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xEJ5Bd3jBcB5"
   },
   "outputs": [],
   "source": [
    "def compute_loss_dnn(x, y, is_eval=False):\n",
    "  d = dnn_eval(x) if is_eval else dnn(x)\n",
    "  nll = -tf.reduce_mean(d.log_prob(y), axis=-1)\n",
    "  return nll, d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CtFMjI6LBuQL"
   },
   "outputs": [],
   "source": [
    "train_iter_dnn = iter(train_dataset)\n",
    "\n",
    "def train_loss_dnn():\n",
    "  x, y = next(train_iter_dnn)\n",
    "  nll, _ = compute_loss_dnn(x, y)\n",
    "  return nll, None\n",
    "\n",
    "opt_dnn = tf.optimizers.Adam(learning_rate=0.003)\n",
    " \n",
    "fit_dnn = tfn.util.make_fit_op(\n",
    "    train_loss_dnn,\n",
    "    opt_dnn,\n",
    "    dnn.trainable_variables,\n",
    "    grad_summary_fn=lambda gs: tf.nest.map_structure(tf.norm, gs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9RWP0V2KbMdP"
   },
   "outputs": [],
   "source": [
    "eval_iter_dnn = iter(eval_dataset.batch(2000).repeat())\n",
    "\n",
    "@tfn.util.tfcompile\n",
    "def eval_dnn(threshold=None):\n",
    "  x, y = next(eval_iter_dnn)\n",
    "  loss, d = compute_loss_dnn(x, y, is_eval=True)\n",
    "  avg_acc, avg_calibration_error, _ = compute_eval_stats(\n",
    "      y, d, threshold=threshold)\n",
    "  return loss, (avg_acc, avg_calibration_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "code",
    "colab": {
     "height": 477
    },
    "colab_type": "code",
    "id": "i5CCUIKXbQ0n",
    "outputId": "a51198c3-2045-4768-d07e-817cace06863"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it:    1   ms/it:2054.2676   tst_acc:0.0755   tst_ece:0.2507   trn_loss:5.2464   tst_loss:4.9561   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:20.3449\n",
      "it:  801   ms/it:2.6896   tst_acc:0.7210   tst_ece:0.1463   trn_loss:0.7330   tst_loss:1.2686   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:4.4537\n",
      "it: 1601   ms/it:2.6657   tst_acc:0.7655   tst_ece:0.1314   trn_loss:0.7336   tst_loss:1.1220   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:5.6005\n",
      "it: 2401   ms/it:2.7007   tst_acc:0.7760   tst_ece:0.1478   trn_loss:1.4435   tst_loss:1.2130   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:7.4838\n",
      "it: 3201   ms/it:2.6893   tst_acc:0.7435   tst_ece:0.1654   trn_loss:0.6468   tst_loss:1.3543   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:4.9734\n",
      "it: 4001   ms/it:2.6818   tst_acc:0.7510   tst_ece:0.1749   trn_loss:0.6040   tst_loss:1.4282   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:4.1424\n",
      "it: 4801   ms/it:2.6974   tst_acc:0.7630   tst_ece:0.1634   trn_loss:0.2504   tst_loss:1.5014   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:4.6321\n",
      "it: 5601   ms/it:2.6929   tst_acc:0.7865   tst_ece:0.1466   trn_loss:0.5419   tst_loss:1.4562   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:5.3873\n",
      "it: 6401   ms/it:2.6761   tst_acc:0.7585   tst_ece:0.1690   trn_loss:0.1518   tst_loss:1.6847   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:2.5078\n",
      "it: 7201   ms/it:2.6877   tst_acc:0.7545   tst_ece:0.1834   trn_loss:0.0498   tst_loss:1.7866   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:1.5773\n",
      "it: 8001   ms/it:2.6489   tst_acc:0.7710   tst_ece:0.1723   trn_loss:1.0039   tst_loss:1.7538   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:8.1693\n",
      "it: 8801   ms/it:2.6540   tst_acc:0.7800   tst_ece:0.1672   trn_loss:0.2140   tst_loss:1.8925   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:3.4027\n",
      "it: 9601   ms/it:2.6657   tst_acc:0.7755   tst_ece:0.1684   trn_loss:0.1703   tst_loss:1.9205   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:3.8770\n",
      "it:10401   ms/it:2.7043   tst_acc:0.7760   tst_ece:0.1669   trn_loss:0.0122   tst_loss:2.0003   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:0.5719\n",
      "it:11201   ms/it:2.6545   tst_acc:0.7545   tst_ece:0.1986   trn_loss:0.2644   tst_loss:2.3727   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:4.5482\n",
      "it:12001   ms/it:2.5525   tst_acc:0.7695   tst_ece:0.1846   trn_loss:0.0530   tst_loss:2.1221   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:1.4701\n",
      "it:12801   ms/it:2.5707   tst_acc:0.7635   tst_ece:0.1829   trn_loss:0.3069   tst_loss:2.3566   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:5.3442\n",
      "it:13601   ms/it:2.6063   tst_acc:0.7745   tst_ece:0.1881   trn_loss:0.1000   tst_loss:2.5953   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:3.8016\n",
      "it:14401   ms/it:2.5316   tst_acc:0.7935   tst_ece:0.1688   trn_loss:0.1445   tst_loss:2.4519   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:3.3261\n",
      "it:15201   ms/it:2.7046   tst_acc:0.7825   tst_ece:0.1840   trn_loss:0.3662   tst_loss:2.7504   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:5.7671\n",
      "it:16001   ms/it:2.6603   tst_acc:0.7630   tst_ece:0.1922   trn_loss:0.2263   tst_loss:2.4021   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:6.4501\n",
      "it:16801   ms/it:2.5350   tst_acc:0.7595   tst_ece:0.2025   trn_loss:0.0164   tst_loss:2.8983   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:1.0847\n",
      "it:17601   ms/it:2.5560   tst_acc:0.7660   tst_ece:0.1951   trn_loss:1.2697   tst_loss:2.8412   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:9.2343\n",
      "it:18401   ms/it:2.5991   tst_acc:0.7875   tst_ece:0.1752   trn_loss:0.1139   tst_loss:2.9975   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:4.4228\n",
      "it:19201   ms/it:2.6893   tst_acc:0.7470   tst_ece:0.2157   trn_loss:0.0020   tst_loss:2.9902   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:0.0976\n",
      "it:20000   ms/it:2.6931   tst_acc:0.7715   tst_ece:0.1970   trn_loss:0.0011   tst_loss:2.8237   tst_nll:3.8186   tst_kl:11.9721   sum_norm_grad:0.0585\n"
     ]
    }
   ],
   "source": [
    "num_train_epochs = 2.  # @param { isTemplate: true}\n",
    "num_evals = 25         # @param { isTemplate: true\n",
    "\n",
    "dur_sec = dur_num = 0\n",
    "num_train_steps = int(num_train_epochs * train_size)\n",
    "for i in range(num_train_steps):\n",
    "  start = time.time()\n",
    "  trn_loss, _, g = fit_dnn()\n",
    "  stop = time.time()\n",
    "  dur_sec += stop - start\n",
    "  dur_num += 1\n",
    "  if i % int(num_train_steps / num_evals) == 0 or i == num_train_steps - 1:\n",
    "    tst_loss, (tst_acc, tst_ece) = eval_dnn()\n",
    "    f, x = zip(*[\n",
    "        ('it:{:5}', opt_dnn.iterations),\n",
    "        ('ms/it:{:6.4f}', dur_sec / max(1., dur_num) * 1000.),\n",
    "        ('tst_acc:{:6.4f}', tst_acc),\n",
    "        ('tst_ece:{:6.4f}', tst_ece),\n",
    "        ('trn_loss:{:6.4f}', trn_loss),\n",
    "        ('tst_loss:{:6.4f}', tst_loss),\n",
    "        ('tst_nll:{:6.4f}', tst_nll),\n",
    "        ('tst_kl:{:6.4f}', tst_kl),\n",
    "        ('sum_norm_grad:{:6.4f}', sum(g)),\n",
    "\n",
    "    ])\n",
    "    print('   '.join(f).format(*[getattr(x_, 'numpy', lambda: x_)()\n",
    "                                 for x_ in x]))\n",
    "    sys.stdout.flush()\n",
    "    dur_sec = dur_num = 0\n",
    "  # if i % 1000 == 0 or i == maxiter - 1:\n",
    "  #   dnn.save('/tmp/dnn.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "5aVSiYt4lT2R"
   },
   "outputs": [],
   "source": [
    "#@title Run Eval\n",
    "eval_iter_dnn = iter(eval_dataset.batch(eval_size))\n",
    "@tfn.util.tfcompile\n",
    "def compute_log_probs_dnn():\n",
    "  x, y = next(eval_iter_dnn)\n",
    "  lp = tf.math.log_softmax(dnn_eval(x).logits, axis=-1)\n",
    "  return x, y, lp\n",
    "x, y, log_probs = compute_log_probs_dnn()\n",
    "\n",
    "\n",
    "\n",
    "max_log_probs = tf.reduce_max(log_probs, axis=-1)\n",
    "\n",
    "idx = tf.argsort(max_log_probs)\n",
    "x = tf.gather(x, idx)\n",
    "y = tf.gather(y, idx)\n",
    "log_probs = tf.gather(log_probs, idx)\n",
    "max_log_probs = tf.gather(max_log_probs, idx)\n",
    "yhat = tf.argmax(log_probs, axis=-1)\n",
    "d = tfd.Categorical(logits=log_probs)\n",
    "hit = tf.cast(tf.equal(y, tf.cast(yhat, y.dtype)), tf.int32)\n",
    "\n",
    "#threshold = 1.-1e-5\n",
    "#keep = tf.where(max_log_probs >= np.log(threshold))[..., 0]\n",
    "keep = tf.range(500, eval_size)\n",
    "\n",
    "x_keep = tf.gather(x, keep)\n",
    "y_keep = tf.gather(y, keep)\n",
    "yhat_keep = tf.gather(yhat, keep)\n",
    "log_probs_keep = tf.gather(log_probs, keep)\n",
    "max_log_probs_keep = tf.gather(max_log_probs, keep)\n",
    "hit_keep = tf.gather(hit, keep)\n",
    "d_keep = tfd.Categorical(logits=log_probs_keep)\n",
    "\n",
    "(\n",
    "    avg_acc, ece,\n",
    "    (acc, conf, cnt, edges, bucket),\n",
    ") = tfn.util.tfcompile(lambda: compute_eval_stats(y, d))()\n",
    "\n",
    "(\n",
    "    avg_acc_keep, ece_keep,\n",
    "    (acc_keep, conf_keep, cnt_keep, edges_keep, bucket_keep),\n",
    ") = tfn.util.tfcompile(lambda: compute_eval_stats(y_keep, d_keep))()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 85
    },
    "colab_type": "code",
    "id": "Z8yGU-b-jZdZ",
    "outputId": "7e777a1f-2b1c-4253-f1a4-d9fb36073520"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples undecided: 500\n",
      "Accurary before excluding undecided ones: 0.7723999619483948\n",
      "Accurary after excluding undecided ones: 0.7929473519325256\n",
      "ECE before/after. 0.19434331 0.19272518\n"
     ]
    }
   ],
   "source": [
    "print('Number of examples undecided: {}'.format(eval_size - tf.size(keep)))\n",
    "print('Accurary before excluding undecided ones: {}'.format(avg_acc))\n",
    "print('Accurary after excluding undecided ones: {}'.format(avg_acc_keep))\n",
    "print('ECE before/after.', ece.numpy(), ece_keep.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 675
    },
    "colab_type": "code",
    "id": "6T0xDQOhja4p",
    "outputId": "74da7e6e-07ab-494f-e1e8-7f07dd4a908a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most uncertain:\n"
     ]
    },
    {
     "data": {
      "image/png": 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UtxC1bdasWdSpU8ft9JKzZ8/mmmuuITQ0lPr16/Pee+95VI97772Xu+++m/DwcI9+X9z4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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1 0 0 1 0 0 0 1 0 1]\n",
      " [1 1 0 1 0 0 1 0 1 0 0 0]\n",
      " [0 0 0 1 0 0 0 0 1 1 0 0]\n",
      " [0 0 1 0 0 0 1 0 1 1 0 0]\n",
      " [1 0 0 0 1 0 0 1 0 0 0 0]\n",
      " [0 0 0 1 0 0 0 0 0 1 0 0]]\n",
      "[['1' 'i' 'I' 'O' 'I' 'I' 'R' 'l' 'I' 'I' 's' '0']\n",
      " ['I' 'r' '5' '9' 'Z' '8' 'I' '1' 'I' 'l' 'l' 'l']\n",
      " ['l' 'l' '2' '1' 'S' 'I' 'I' '8' '1' 'I' '0' '0']\n",
      " ['1' 'I' '1' 'I' 'j' 'a' '1' '1' '3' 'l' 'y' '0']\n",
      " ['i' '1' 'I' '1' 'N' 'I' '0' '0' 'u' 'I' '1' 'V']\n",
      " ['0' 'I' 'z' 'O' 'R' 'l' '0' 'y' '0' 'l' '0' 't']]\n"
     ]
    }
   ],
   "source": [
    "print('Most uncertain:')\n",
    "ss = (6,12); n = np.prod(ss); s = ss+image_shape\n",
    "tfn.util.display_imgs(\n",
    "    tf.reshape(x[:n], s),\n",
    "    yhuman[tf.reshape(y[:n], ss).numpy()])\n",
    "print(tf.reshape(hit[:n], ss).numpy())\n",
    "print(yhuman[tf.reshape(yhat[:n], ss).numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 675
    },
    "colab_type": "code",
    "id": "lcibcRp4jcHV",
    "outputId": "8cfd08cf-c2bd-408c-fb23-4fd1b8cba137"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Least uncertain:\n"
     ]
    },
    {
     "data": {
      "image/png": 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r1kSdOnUMtlFRUlJSsGzZMoM7i/qsXLkSsbGxWL9+PczMDH+UWVtbY/jw4Vi0aBFOnz5t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ASgNYTdZMyIcyguLnbrir/F24jXiH2pUKv+RcE5w/rHIwgDSt8+8MADklIZXqo6j9m39913X0m+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\n/wFMwUqsRwdilQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1 1 1 1 1 1 1 1 1 0]\n",
      " [1 1 1 1 1 1 1 0 1 1 0 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [0 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1]]\n",
      "[['P' 'e' 'r' 'P' '7' 'e' '3' '6' 'H' '9' '5' 'U']\n",
      " ['2' '6' '5' 'e' '6' '6' '2' 'y' 'T' '4' '2' '3']\n",
      " ['S' 'C' '3' '8' 'n' '3' 'b' '9' '7' '6' '3' '6']\n",
      " ['5' '5' '9' 'Z' '3' 'Z' '8' 'T' '7' '8' '5' '2']\n",
      " ['0' '6' '7' 'r' '3' 'C' 'Q' 'C' '3' '9' '6' '6']\n",
      " ['9' 'B' '9' 'M' '4' '7' '8' '3' '6' '4' '8' 'a']]\n"
     ]
    }
   ],
   "source": [
    "print('Least uncertain:')\n",
    "tfn.util.display_imgs(\n",
    "    tf.reshape(x[-n:], s),\n",
    "    yhuman[tf.reshape(y[-n:], ss).numpy()])\n",
    "print(tf.reshape(hit[-n:], ss).numpy())\n",
    "print(yhuman[tf.reshape(yhat[-n:], ss).numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 269
    },
    "colab_type": "code",
    "id": "k_x1Mvws5pXP",
    "outputId": "be2b6146-5d30-4d07-bc84-90d2baa30326"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VKuLj4y97rby8nA0bNgCwYcMG9uzZ4399/fr1OBwOMjIyyMzMpLKyMgDDFhEJP+7RWyB4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\n28IklYcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = max_log_probs + tf.math.log1p(-max_log_probs); b=tf.boolean_mask(b,b<-1e-12)\n",
    "sns.distplot(tf.math.exp(b).numpy(), bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {
     "height": 51
    },
    "colab_type": "code",
    "id": "9M8hFsvLlymd",
    "outputId": "68ac5784-6bc0-486d-b8d3-766d191eafed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg per class AUC:\n",
      "0.9858841557475477\n"
     ]
    }
   ],
   "source": [
    "#@title Avg One-vs-Rest AUC\n",
    "try:\n",
    "  dnn_auc = sklearn_metrics.roc_auc_score(\n",
    "      y_keep,\n",
    "      log_probs_keep,\n",
    "      average='macro',\n",
    "      multi_class='ovr')  \n",
    "  print('Avg per class AUC:\\n{}'.format(dnn_auc))\n",
    "except TypeError:\n",
    "  dnn_auc = np.array([\n",
    "    sklearn_metrics.roc_auc_score(tf.equal(y_keep, i), log_probs_keep[:, i])\n",
    "    for i in range(num_classes)])\n",
    "  print('Avg per class AUC:\\n{}'.format(dnn_auc.mean()))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "B0HrNKbJw2bA",
    "nbQ3rcTowypZ",
    "a_LR5N47a1ce"
   ],
   "name": "Bayesian Neural Network",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
